Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations750
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.6 KiB
Average record size in memory88.2 B

Variable types

Numeric6
Categorical5

Alerts

HiringDecision is highly overall correlated with RecruitmentStrategyHigh correlation
RecruitmentStrategy is highly overall correlated with HiringDecisionHigh correlation
DistanceFromCompany has unique values Unique
ExperienceYears has 48 (6.4%) zeros Zeros
InterviewScore has 13 (1.7%) zeros Zeros

Reproduction

Analysis started2024-11-25 21:46:57.880866
Analysis finished2024-11-25 21:47:02.422972
Duration4.54 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct31
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.898667
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:47:02.501508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21
Q126
median35
Q343
95-th percentile49
Maximum50
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.2165969
Coefficient of variation (CV)0.26409596
Kurtosis-1.2828679
Mean34.898667
Median Absolute Deviation (MAD)8
Skewness-0.029473971
Sum26174
Variance84.945659
MonotonicityNot monotonic
2024-11-25T18:47:02.642636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
45 35
 
4.7%
24 33
 
4.4%
47 31
 
4.1%
43 31
 
4.1%
38 30
 
4.0%
20 29
 
3.9%
34 28
 
3.7%
21 28
 
3.7%
23 27
 
3.6%
26 27
 
3.6%
Other values (21) 451
60.1%
ValueCountFrequency (%)
20 29
3.9%
21 28
3.7%
22 26
3.5%
23 27
3.6%
24 33
4.4%
25 23
3.1%
26 27
3.6%
27 26
3.5%
28 16
2.1%
29 16
2.1%
ValueCountFrequency (%)
50 22
2.9%
49 26
3.5%
48 21
2.8%
47 31
4.1%
46 20
2.7%
45 35
4.7%
44 21
2.8%
43 31
4.1%
42 19
2.5%
41 25
3.3%

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
0
388 
1
362 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 388
51.7%
1 362
48.3%

Length

2024-11-25T18:47:02.785628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:47:02.894238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 388
51.7%
1 362
48.3%

Most occurring characters

ValueCountFrequency (%)
0 388
51.7%
1 362
48.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 388
51.7%
1 362
48.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 388
51.7%
1 362
48.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 388
51.7%
1 362
48.3%

EducationLevel
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2
359 
3
164 
1
162 
4
65 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 359
47.9%
3 164
21.9%
1 162
21.6%
4 65
 
8.7%

Length

2024-11-25T18:47:03.008033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:47:03.141048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 359
47.9%
3 164
21.9%
1 162
21.6%
4 65
 
8.7%

Most occurring characters

ValueCountFrequency (%)
2 359
47.9%
3 164
21.9%
1 162
21.6%
4 65
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 359
47.9%
3 164
21.9%
1 162
21.6%
4 65
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 359
47.9%
3 164
21.9%
1 162
21.6%
4 65
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 359
47.9%
3 164
21.9%
1 162
21.6%
4 65
 
8.7%

ExperienceYears
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6386667
Minimum0
Maximum15
Zeros48
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:47:03.261908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q312
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.581713
Coefficient of variation (CV)0.59980534
Kurtosis-1.1528004
Mean7.6386667
Median Absolute Deviation (MAD)4
Skewness-0.045316043
Sum5729
Variance20.992094
MonotonicityNot monotonic
2024-11-25T18:47:03.566683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 54
 
7.2%
9 53
 
7.1%
15 51
 
6.8%
6 51
 
6.8%
10 50
 
6.7%
0 48
 
6.4%
14 47
 
6.3%
11 46
 
6.1%
1 46
 
6.1%
12 46
 
6.1%
Other values (6) 258
34.4%
ValueCountFrequency (%)
0 48
6.4%
1 46
6.1%
2 38
5.1%
3 41
5.5%
4 45
6.0%
5 45
6.0%
6 51
6.8%
7 54
7.2%
8 44
5.9%
9 53
7.1%
ValueCountFrequency (%)
15 51
6.8%
14 47
6.3%
13 45
6.0%
12 46
6.1%
11 46
6.1%
10 50
6.7%
9 53
7.1%
8 44
5.9%
7 54
7.2%
6 51
6.8%
Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
4
160 
2
156 
5
146 
3
146 
1
142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row4
4th row5
5th row3

Common Values

ValueCountFrequency (%)
4 160
21.3%
2 156
20.8%
5 146
19.5%
3 146
19.5%
1 142
18.9%

Length

2024-11-25T18:47:03.708489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:47:03.831603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 160
21.3%
2 156
20.8%
5 146
19.5%
3 146
19.5%
1 142
18.9%

Most occurring characters

ValueCountFrequency (%)
4 160
21.3%
2 156
20.8%
5 146
19.5%
3 146
19.5%
1 142
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 160
21.3%
2 156
20.8%
5 146
19.5%
3 146
19.5%
1 142
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 160
21.3%
2 156
20.8%
5 146
19.5%
3 146
19.5%
1 142
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 160
21.3%
2 156
20.8%
5 146
19.5%
3 146
19.5%
1 142
18.9%

DistanceFromCompany
Real number (ℝ)

Unique 

Distinct750
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.261624
Minimum1.0313758
Maximum50.973395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:47:03.973175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.0313758
5-th percentile3.5068797
Q112.886118
median25.365458
Q337.376079
95-th percentile47.782277
Maximum50.973395
Range49.942019
Interquartile range (IQR)24.489961

Descriptive statistics

Standard deviation14.399744
Coefficient of variation (CV)0.5700245
Kurtosis-1.1985488
Mean25.261624
Median Absolute Deviation (MAD)12.06103
Skewness0.030917035
Sum18946.218
Variance207.35263
MonotonicityNot monotonic
2024-11-25T18:47:04.121438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.4973248 1
 
0.1%
26.13150578 1
 
0.1%
1.342037407 1
 
0.1%
18.83044564 1
 
0.1%
43.99703571 1
 
0.1%
32.11514895 1
 
0.1%
9.183783186 1
 
0.1%
47.72701228 1
 
0.1%
24.13051516 1
 
0.1%
15.11569171 1
 
0.1%
Other values (740) 740
98.7%
ValueCountFrequency (%)
1.031375831 1
0.1%
1.037385419 1
0.1%
1.063403846 1
0.1%
1.109207143 1
0.1%
1.116170307 1
0.1%
1.197997151 1
0.1%
1.243420383 1
0.1%
1.342037407 1
0.1%
1.352145731 1
0.1%
1.357104088 1
0.1%
ValueCountFrequency (%)
50.97339471 1
0.1%
50.89887278 1
0.1%
50.79968246 1
0.1%
50.78095298 1
0.1%
50.7755484 1
0.1%
50.63127742 1
0.1%
50.59158962 1
0.1%
50.42452422 1
0.1%
50.32437008 1
0.1%
50.14300589 1
0.1%

InterviewScore
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.952
Minimum0
Maximum100
Zeros13
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:47:04.276043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q124
median50
Q373
95-th percentile94
Maximum100
Range100
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.653453
Coefficient of variation (CV)0.58533775
Kurtosis-1.1872029
Mean48.952
Median Absolute Deviation (MAD)25
Skewness0.003376605
Sum36714
Variance821.02039
MonotonicityNot monotonic
2024-11-25T18:47:04.432818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 14
 
1.9%
60 14
 
1.9%
72 13
 
1.7%
0 13
 
1.7%
30 12
 
1.6%
36 12
 
1.6%
82 12
 
1.6%
56 11
 
1.5%
32 11
 
1.5%
14 11
 
1.5%
Other values (91) 627
83.6%
ValueCountFrequency (%)
0 13
1.7%
1 6
0.8%
2 8
1.1%
3 8
1.1%
4 2
 
0.3%
5 5
 
0.7%
6 6
0.8%
7 8
1.1%
8 8
1.1%
9 6
0.8%
ValueCountFrequency (%)
100 7
0.9%
99 6
0.8%
98 7
0.9%
97 6
0.8%
96 3
 
0.4%
95 4
0.5%
94 6
0.8%
93 7
0.9%
92 7
0.9%
91 9
1.2%

SkillScore
Real number (ℝ)

Distinct101
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.489333
Minimum0
Maximum100
Zeros7
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:47:04.584402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median51
Q375.75
95-th percentile94.55
Maximum100
Range100
Interquartile range (IQR)50.75

Descriptive statistics

Standard deviation29.365734
Coefficient of variation (CV)0.58162254
Kurtosis-1.2202508
Mean50.489333
Median Absolute Deviation (MAD)25
Skewness-0.041105341
Sum37867
Variance862.34635
MonotonicityNot monotonic
2024-11-25T18:47:04.745856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 14
 
1.9%
70 14
 
1.9%
16 13
 
1.7%
60 13
 
1.7%
94 13
 
1.7%
40 12
 
1.6%
28 12
 
1.6%
92 12
 
1.6%
68 12
 
1.6%
34 12
 
1.6%
Other values (91) 623
83.1%
ValueCountFrequency (%)
0 7
0.9%
1 4
 
0.5%
2 8
1.1%
3 10
1.3%
4 7
0.9%
5 10
1.3%
6 11
1.5%
7 6
0.8%
8 8
1.1%
9 10
1.3%
ValueCountFrequency (%)
100 7
0.9%
99 6
0.8%
98 4
 
0.5%
97 10
1.3%
96 6
0.8%
95 5
 
0.7%
94 13
1.7%
93 7
0.9%
92 12
1.6%
91 9
1.2%

PersonalityScore
Real number (ℝ)

Distinct101
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.442667
Minimum0
Maximum100
Zeros5
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:47:04.907268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median47.5
Q375
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.878296
Coefficient of variation (CV)0.58407643
Kurtosis-1.1955691
Mean49.442667
Median Absolute Deviation (MAD)24.5
Skewness0.060759086
Sum37082
Variance833.95599
MonotonicityNot monotonic
2024-11-25T18:47:05.059749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 13
 
1.7%
94 13
 
1.7%
86 13
 
1.7%
35 12
 
1.6%
25 12
 
1.6%
92 11
 
1.5%
14 11
 
1.5%
47 11
 
1.5%
16 11
 
1.5%
44 11
 
1.5%
Other values (91) 632
84.3%
ValueCountFrequency (%)
0 5
0.7%
1 4
 
0.5%
2 8
1.1%
3 7
0.9%
4 9
1.2%
5 7
0.9%
6 5
0.7%
7 8
1.1%
8 5
0.7%
9 10
1.3%
ValueCountFrequency (%)
100 5
 
0.7%
99 7
0.9%
98 6
0.8%
97 5
 
0.7%
96 9
1.2%
95 7
0.9%
94 13
1.7%
93 5
 
0.7%
92 11
1.5%
91 7
0.9%

RecruitmentStrategy
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2
374 
1
228 
3
148 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 374
49.9%
1 228
30.4%
3 148
 
19.7%

Length

2024-11-25T18:47:05.213376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:47:05.332403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 374
49.9%
1 228
30.4%
3 148
 
19.7%

Most occurring characters

ValueCountFrequency (%)
2 374
49.9%
1 228
30.4%
3 148
 
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 374
49.9%
1 228
30.4%
3 148
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 374
49.9%
1 228
30.4%
3 148
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 374
49.9%
1 228
30.4%
3 148
 
19.7%

HiringDecision
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
0
518 
1
232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 518
69.1%
1 232
30.9%

Length

2024-11-25T18:47:05.462976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:47:05.571064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 518
69.1%
1 232
30.9%

Most occurring characters

ValueCountFrequency (%)
0 518
69.1%
1 232
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 518
69.1%
1 232
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 518
69.1%
1 232
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 518
69.1%
1 232
30.9%

Interactions

2024-11-25T18:47:01.482634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:58.190788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:58.860222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.539741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.164440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.830290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.587688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:58.293066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:58.984095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.644351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.277065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.943361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.693043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:58.410631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.107301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.748999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.403620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.064749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.793466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:58.526681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.224112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.847942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.512146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.169684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.893986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:58.634183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.334615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.947649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.623964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.277552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.995514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:58.745487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:59.437558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.057382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:00.724146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:47:01.382539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-25T18:47:05.650468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeDistanceFromCompanyEducationLevelExperienceYearsGenderHiringDecisionInterviewScorePersonalityScorePreviousCompaniesRecruitmentStrategySkillScore
Age1.000-0.0050.0450.0060.0000.0840.0140.0170.0280.105-0.017
DistanceFromCompany-0.0051.0000.063-0.0030.0000.002-0.0150.0150.0000.071-0.007
EducationLevel0.0450.0631.0000.0000.0320.2930.0530.0000.0000.0460.000
ExperienceYears0.006-0.0030.0001.0000.0000.113-0.0750.0090.0000.000-0.030
Gender0.0000.0000.0320.0001.0000.0000.0340.0000.0480.0000.000
HiringDecision0.0840.0020.2930.1130.0001.0000.2360.1890.0000.5720.182
InterviewScore0.014-0.0150.053-0.0750.0340.2361.000-0.0290.0000.0400.004
PersonalityScore0.0170.0150.0000.0090.0000.189-0.0291.0000.0000.000-0.039
PreviousCompanies0.0280.0000.0000.0000.0480.0000.0000.0001.0000.0000.044
RecruitmentStrategy0.1050.0710.0460.0000.0000.5720.0400.0000.0001.0000.000
SkillScore-0.017-0.0070.000-0.0300.0000.1820.004-0.0390.0440.0001.000

Missing values

2024-11-25T18:47:02.136932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-25T18:47:02.332231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderEducationLevelExperienceYearsPreviousCompaniesDistanceFromCompanyInterviewScoreSkillScorePersonalityScoreRecruitmentStrategyHiringDecision
032039543.49732559397211
126143136.68260477494311
2210113422.5994691601620
347124550.8988736792920
44312834.21192950447720
531021026.02287754558611
62612857.2306697898520
7410214442.77701140981220
832049224.15997248925630
949013322.78360331603910
AgeGenderEducationLevelExperienceYearsPreviousCompaniesDistanceFromCompanyInterviewScoreSkillScorePersonalityScoreRecruitmentStrategyHiringDecision
740200315211.1436598736911
741200413323.23138363854920
74232039210.47595260664921
743250113419.586349165520
74422137538.2298490618811
74527135441.33867899507111
74642035322.56052497371920
74747036338.4349249953211
74824123545.1411582025410
749310314527.6654181872420